title: Video segmentation of moving humans for assistive environments.
abstract: In this paper, we present two recently proposed efficient methods for human segmentation from video in indoor environments: the illumination sensitive background method and the self-organizing background subtraction (SOBS) method. Both methods maintain multiple background models. The SOBS method has been modified in this work for gray-scale frames, in order to decrease processing times. The video data are acquired indoors from a fixed fish-eye camera in the living environment. The paper presents the algorithmic implementation and modifications details, while results are also presented for a small number of video sequences.
authors.affiliation: Department of Digital Systems, University of Piraeus, Greece.
authors.forename: Ilias
authors.initials: I
authors.lastname: Maglogiannis
authors.forename: K K
authors.initials: KK
authors.lastname: Delibasis
authors.forename: T
authors.initials: T
authors.lastname: Goudas
authors.forename: Andriana
authors.initials: A
authors.lastname: Prentza
authors.forename: Flora
authors.initials: F
authors.lastname: Malamateniou
authors.forename: George
authors.initials: G
authors.lastname: Vassilacopoulos
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018670:Monitoring, Ambulatory / Q000379:methods; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D015203:Reproducibility of Results; D012656:Self-Help Devices; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods*